Learning computer element



Feb 17, 1970 c. g. HENDRIX 3,496,382

LEARNING COMPUTERELEMENT Filed May 12, 1967 2 Sheets-Sheet 1 Fla. 1 I:"Pu 8 l4 lnhlbltory Output Umvabraizor Exc'tatopy Output '6Con'lrollable & Threshold 2| Resusiorw- Term Memory \20 Gate 22 {i sw'khPunish-Reward Bus (Common 15 All EIemen'Es) x .-|'.O-.O 0; o Path Q Pathb Y v 3 A A O Y2 X. Y, Y

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INVENTOR.

Charles E. Hendnx Feb. 17, 1970 c. s. HENDRIX 3,496,382

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H O INVENTOR.

Charles E. Hendrax L. BY g g United States Patent U.S. Cl. 307-401 3Claims ABSTRACT OF THE DISCLOSURE This invention relates to adaptivecomputer technology and more particularly to electronic circuitry forsimulating neural functions. I have developed an electronic circuitcapable of simulating all of the significant functional properties ofneurons.

BACKGROUND OF THE INVENTION Field of the invention The development ofmathematical or physical models of neurons serve dual purposes ofincreasing our understanding of neural action and to produce ageneration of computing devices having the adaptive or learnmgcapability of neurons.

Description of the prior art One principal characteristic of the neuronis the property of axon discharge which allows the conduction ofimpulses over a distance. This capability has been simulated in theelectrochemical systems disclosed in the R. M. Stewart Patents 3,149,310and 3,295,112 assigned to the assignee of this invention, or more simplyby a copper conductor. Functional properties of the neuron moredlflicult to simulate are:

(a) The neuron responds to a stimulus with an electrical pulse ofstandard size and shape. If the stimulus continues, the pulses occur atregular intervals with the rate of occurrence dependent on the intensityof stimulation.

(b) There is a threshold of stimulation. If the intensity of thestimulus is below this threshold, the neuron does not fire.

(c) The neuron is capable of temporal and spartial integration. Manysub-threshold stimuli arriving at the neuron from difierent sources, orat slightly different times, can add up to a sufiicient level to firethe neuron.

(d) Some inputs are excitatory, some are inhibitory.

(c) There is a refractory period. Once fired, there is a subsequentperiod during which the neuron cannot be fired again, no matter howlarge the stimulus. This places an upper limit on the pulse rate of anyparticular neuron.

(f) The neuron can learn. This property is conjectural in livingneurons, since it appears that at the present time learning has not beenclearly demonstrated in isolated living neurons. However, the learningproperty is basic to all self-organizing models.

Neuron models with the above characteristics have been built, althoughnone seem to have incorporated all of them in a single model. Harmon etal. have built neuron models which have the characteristics (a) through(e), with which he has built extremely interesting devices whichsimulate portions of the peripheral neuron system. These are reported inHarmon, L. D., Levinson, 1., and Van Bergeijk,.W.A.. Analog Models ofNeural Mechanism, IRE Trans. On Information Theory IT-8: 107-112(February 1962).

Various attempts at learning elements have been made, perhaps bestexemplified by those of Widrow, B., and Hoff, M.E., Adaptive SwitchingCircuits, Stanford Electronics Lab Tech Report 1553-1, June 1960. Thesedevices 3,496,382 Patented Feb. 17, 1970 ICC of a neuron, it is possibleto synthesize a simple model which has all of them.

SUMMARY OF THE INVENTION Basically, the invention involves a discreteelectronic circuit having a number of input terminals which are combinedin a summing network and constituting the triggering input to monostablemultivibrator. This circuit provides simulation of the fivecharacteristics (a) (e) above. The adaptive or learning function ((f)above) is provided when the circuit includes an outside input whichdetermines whether the last response of the circuit was desirable ornot. If it was desirable, the response threshold is lowered, making iteasier to respond the next time. If the result was not desirable, thethreshold is raised, making it more difficult for the neuron to fire thenext time. Electronically, this is accomplished by routing the output ofthe monostable multivibrator through a shortterm memory device. Thismemory which may merely be a pulse stretcher circuit, temporarilyrecords the fact that the neuron has recently fired. This short-termmemory controls a gate which either accepts or rejects an externalcontrol (punish-reward) signal used to control a variable resistor inthe summing circuit.

This combination contains, in a relatively simple electronic circuit,the necessary properties to react like a neuron to external stimuli.Whena number of these circuits are connected in a net, the circuits arelikewise able to exhibit the properties of neural nets.

BRIEF DESCRIPTION OF THE DRAWING This invention and its features may bemore clearly understood from the following description and by referenceto the drawing in which:

FIG. 1 is a block diagram of this invention;

FIG. 2 is a schematic representation of a portion of a net employing thecircuit of FIG. 1;

FIG. 3 constitutes three fragmentary portions of the net of FIG. 2illustrating alternate signal paths through the net of FIG. 2; and,

FIG. 4 is an electrical schematic diagram of a circuit meeting thecharacteristics of FIG. 1.

DESCRIPTION OF THE PREFERRED EMBODIMENTS Now referring to the drawing,in FIG. 1, a single computer element 10 or artificial neuron is showncomprising a plurality of input terminals connected through respectiveisolating input resistors 11 to a common input line 12 used for summingthe incoming signal. The total current flows through the thresholdresistor 13, and the voltage appearing on the input line 12 is theproduct of the sum of the input currents and the value of the thresholdresistor.

The input line 12 constitutes the trigger signal source for a monostablemultivibrator or univibrator 1 4. Tlns later has a pair of output leads'15 and 16, one an inhibitory output and the second an excitatoryoutput. The output leads may either produce opposite polarity pulses ormay provide their function by their mode of connection to the succeedingstage or utilization device. Where computer units 10 are connected in anet or series similar to FIG. 2 below, the output leads 15 and 16 wouldnormally be of opposite polarity as can easily be derived from oppositeswitching elements in the conventional univibrator 14.

The excitatory output 16 is additionally connected to t short termmemory device or pulse stretcher 20 which :erves as an enabling input toa coincidence or AND gate 21. The second input to AND gate 21 is overlead 52 from a common bus 23 under control of an external awitch 24,here designated as the punish-reward switch. [he switch 24 is used toapply a signal through the AND gate 21 to the variable resistance 13associated with the :umming network 12.

In operation, the computer element of this invention s connected witheach of its n number of inputs conlected to information sources. Thesources may provide ;ignals, either analog or discrete level,simultaneous, timed )r random. In any case, the signals are summed andap- Jlied to the switching input of the univibrator 14. The iignal levelof lead 12 at the univibrator 14 control in- )ut is a function of thesummation of all inputs 10 and :he resistance R of controllablethreshold resistance 13. If this summation exceeds the switchingthreshold of mivibrator 14, it will operate and produce an output pulse)n lead 16 indicative of a positive response. At the same Lime, anoutput pulse for example, the excitatory pulse, s temporarily stored inmemory 20 and during the storage period AND gate is enabled fortransmission of a ?-R signal to modify the resistance R of element 13.If 1 P-R signal indicative of a favorable response (reward) ls appliedto lead 23 to adjust the threshold of resistor [3, it allows easiertriggering of the univibrator 14. Con- ICISCIY if the punish switch isoperated during the eniblement period, the threshold is adjusted in anopposite sense. If no P-R switch input occurs during the enablenentperiod, the threshold remains unchanged.

As just described, the computer element 10 exhibits 111 of the neutralproperties catalogued above, and in particular is adaptive or capable oflearning, owing to the presence of the memory 20, AND gate 21 andvariable hreshold resistance 13.

When a number of similar elements 10 are connected nto a net asillustrated in FIG. 2, the elements can form a. logically completesystem. This is accomplished if aome form of negation is included.Negation can be provided where opposite polarity pulses or signals areapplied to indicate excitatory or inhibitory information 1nd oppositepolarity output from each element 10 are ised as the respectiveinhibitory excitator inputs to subiequent stages. This arrangement isillustrated in FIG. 2 vhere a discrete input element 10 is representedas X 1nd a corresponding output element 10 is designated as Y. In thesmall fragment of a net shown, a plurality of dgnal conduction pathsexist between elements X and Y. Four of such paths are illustrated inFIG. 3. Adding the nirror image paths to B, C and D gives a total ofseven :ignal paths between X and Y where only excitatory sig- 1alsemanate from any of the units. Where a number of :onduction pathsbetween adjacent elements are selected 18 inhibitory, the performance ofthe net is varied signiicantly. For example, the net may be connectedsuch that .he arrows indicate excitatory inputs and the arrows with 1crossline as inhibitory. In such a case, the number )f possible paths isgreatly reduced and the element Y will not respond to a simple signalinput to element X tlone but only when a pattern of simultaneous or near;imultaneous signals arrive from a number of input elenents 10. Thus,the device becomes a pattern recognition ;ystem. In the illustrationgiven, paths A, C and D are noperative and path B is operative whenthere is an input X and no input to X or there is an input to X andinput to X or there is an input to all three. Path B will not beoperative if there is an input to X only, )1 to X and either of X or Xbut not both. As the let becomes more complex and the effects of P-Rinputs are considered, the information handling capacity .ncreasesastronomically. Regardless of the complexity, :he net exhibits adaptiveproperties where the bus 24 s connected in common to all elements asillustrated by the dash lines of FIG. 2. The operation of the P-R switchalfects only the elements which have recently been active to change theresponse of the net in the desired manner.

In all of the descriptionsof this invention, the terms excitatory andinhibitory signals denote the effect upon succeeding or controlledelements and not the particular polarity of form of signal. It isrecognized that the same signal can be used to enable or disable aconduction or switching device depending upon its mode of application.Therefore, inverting amplifiers or other signal processors can beselectively used to convert the same signal to opposite form andopposite function without departing from the concept of this invention.

Consider a network of elements 10 connected together in some fashion.Some of the connections will be excitatory and some inhibitory. Some ofthe elements 10 will be designated as inputs, and some outputs. Now leta stimulus be applied to the net. Some response will occur, and let usassume that it is a desirable response. Then the trainer will reward thenet by momentary operation of the reward switch 24. This will lower thethresholds of those elements 10 which took part in the response, andwill make it easier for them to respond in the same way to similarinputs.

On the other hand, if the response was not the desired one, the trainerwill punish the net by momentary operation of swtch 24 to the punishmentposition, raising the thresholds of the responding units, and making itmore difiicult for them to respond a second time. Punishment will havean additional effect, however. Some of the responding elements will havebeen inhibiting other members of the net. When the inhibition sourcesare turned 011?, these newly-freed elements of the net may respond.Hence, punishment has the effect of making the net try new solutions tothe same problem. It will therefore search for a response which isacceptable to the trainer, never retracing its path in solution space.This is what we mean by learned behavior.

As described above in FIG. 1, the computer element is made up of anumber of recognized electronic circuits of known design. In theproduction of neural nets of meaningful compactness to informationhandling capacity, microcircuit techniques are highly advantageous. Inparticular, an entire neural net may be constructed as a singleintegrated circuit to achieve volumetric efficiency. For purposes ofillustration, the principles of this invention may be reproduced usingconventional discrete element circuitry in which case typical componentsand values may be:

Input resistors 11 Univibrator 14 l OK ohms.

Unijunction transistor 2N1671A balanced circuit with 2Nl308 driverstage.

Short term memory 20 2N1309NPN transistor phase Bi-directional ANDsplitter.

gate 21 Diode bridge. Controllable threshold resistor Solion cell. TexasRes. & Elec.

Corp. SE-l 10.

The above specific implementation is illustrated in FIG. 4. Obvioussubstitution for the configuration of the particular univibrator,short-term memory and AND gate will be apparent to those skilled in theart. The controllable threshold resistor 13, however, requires that thedevice be variable in resistance (electrically preferably) reversibleand stable. Without resort to electromechanical devices which areimpractical in complex nets, the solion cell is considered preferred.Also suitable is the ferroelectric field effect device described by P.M. Heymann et al. in vol. 54 of the Proceedings of the IEEE, pages842848 (June 1966).

The foregoing is a description of one or more embodiments of myinvention. It is recognized that one skilled in the art can devisevariations from the specific forms in which my invention is illustrated.In accordance with the patent laws of the United States, the rightsgranted thereunder are not limited to the specific embodimentsillustrated, but rather by the scope of the following claims and theirequivalents.

What is claimed is:

1. An artificial neuron comprising a circuit including:

a plurality of inputs;

means for summing said inputs;

means responsive to summed inputs above a threshold for producing asignal;

means for storing the signal from said signal producing means;

an external control signal source; and,

gate means for passing the signals from said storing means and saidexternal source to control said threshold means.

2. The combination in accordance with claim 1, wherein said externalsource is capable of producing two discrete signals representative of afavored and of an unfavored response.

3. The combination in accordance with c aim 2, wherein said thresholdmeans responds to one of said discrete signals from said gate means toincrease the threshold of said signal producing means and to the secondof said discrete signals from said gate means to lower the threshold ofsaid signal producing means.

References Cited UNITED STATES PATENTS OTHER REFERENCES Coates et al.,Design of a Solid-State Neuron Circuit for Use in Self-OrganizingSystems, 1960 International Solid-State Circuits Conference, Digest ofTechnical Papers, pp. 39, 40.

Morafr, Artificial Neurons, Electronics Industries, December 1963, pp.52-56.

PAUL J. HENON, Primary Examiner R. F. CHAPURAN, Assistant Examiner U.S.Cl. X.R.

